有色环境噪音对空间异质种群动态同步性的影响

随着种群动态和空间结构研究兴趣的增加,激发了大量的有关空间同步性的理论和实验的研究工作。空间种群的同步波动现象在自然界广泛存在,它的影响和原因引起了很多生态学家的兴趣。Moran定理是一个非常重要的解释。但以往的研究大多假设环境变化为空间相关的白噪音。越来越多的研究表明很多环境变化的时间序列具有正的时间自相关性,也就是说用红噪音来描述更加合理。因此,推广经典的Moran效应来处理空间相关红噪音的情形很有必要。利用线性的二阶自回归过程的种群模型,推导了两种群空间同步性与种群动态异质性和环境变化的时间相关性(即环境噪音的颜色)之间的关系。深入分析了种群异质性和噪音颜色对空间同步性的影响。结果表明种群动态异质性不利于空间同步性,但详细的关系比较复杂。而红色噪音的同步能力体现在两方面:一方面,本身的相关性对同步性有贡献;另一方面,环境变化时间相关性可以通过改变种群密度依赖来影响同步性,但对同步性的影响并无一致性的结论,依赖于种群的平均动态等因素。这些结果对理解同步性的机理、利用同步机理来制定物种保护策略和害虫防治都有重要的意义。     

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time‐course omics data

Omics experiments endowed with a time‐course design may enable us to uncover the dynamic interplay among genes of cellular processes. Multivariate techniques (like VAR(1) models describing the temporal and contemporaneous relations among variates) that may facilitate this goal are hampered by the high‐dimensionality of the resulting data. This is resolved by the presented ridge regularized maximum likelihood estimation procedure for the VAR(1) model. Information on the absence of temporal and contemporaneous relations may be incorporated in this procedure. Its computational efficient implemention is discussed. The estimation procedure is accompanied with an LOOCV scheme to determine the associated penalty parameters. Downstream exploitation of the estimated VAR(1) model is outlined: an empirical Bayes procedure to identify the interesting temporal and contemporaneous relationships, impulse response analysis, mutual information analysis, and covariance decomposition into the (graphical) relations among variates. In a simulation study the presented ridge estimation procedure outperformed a sparse competitor in terms of Frobenius loss of the estimates, while their selection properties are on par. The proposed machinery is illustrated in the reconstruction of the p53 signaling pathway during HPV‐induced cellular transformation. The methodology is implemented in the ragt2ridges R‐package available from CRAN.     

Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models

In this paper, we propose a test procedure to detect change points of multidimensional autoregressive processes. The considered process differs from typical applied spatial autoregressive processes in that it is assumed to evolve from a predefined center into every dimension. Additionally, structural breaks in the process can occur at a certain distance from the predefined center. The main aim of this paper is to detect such spatial changes. In particular, we focus on shifts in the mean and the autoregressive parameter. The proposed test procedure is based on the likelihood‐ratio approach. Eventually, the goodness‐of‐fit values of the estimators are compared for different shifts. Moreover, the empirical distribution of the test statistic of the likelihood‐ratio test is obtained via Monte Carlo simulations. We show that the generalized Gumbel distribution seems to be a suitable limiting distribution of the proposed test statistic. Finally, we discuss the detection of lung cancer in computed tomography scans and illustrate the proposed test procedure.     

Granger mediation analysis of multiple time series with an application to functional magnetic resonance imaging

This paper presents Granger mediation analysis, a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The independent observation assumption is thus unrealistic for this type of time‐series data. To address this challenge, our framework integrates two types of models: causal mediation analysis across the mediation variables, and vector autoregressive (VAR) models across the temporal observations. We use “Granger” to refer to VAR correlations modeled in this paper. We further extend this framework to handle multilevel data, in order to model individual variability and correlated errors between the mediator and the outcome variables. Using Rubin's potential outcome framework, we show that the causal mediation effects are identifiable under our time‐series model. We further develop computationally efficient algorithms to maximize our likelihood‐based estimation criteria. Simulation studies show that our method reduces the estimation bias and improves statistical power, compared with existing approaches. On a real fMRI data set, our approach quantifies the causal effects through a brain pathway, while capturing the dynamic dependence between two brain regions.     

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions

Linear models are typically used to analyze multivariate longitudinal data. With these models, estimating the covariance matrix is not easy because the covariance matrix should account for complex correlated structures: the correlation between responses at each time point, the correlation within separate responses over time, and the cross-correlation between different responses at different times. In addition, the estimated covariance matrix should satisfy the positive definiteness condition, and it may be heteroscedastic. However, in practice, the structure of the covariance matrix is assumed to be homoscedastic and highly parsimonious, such as exchangeable or autoregressive with order one. These assumptions are too strong and result in inefficient estimates of the effects of covariates. Several studies have been conducted to solve these restrictions using modified Cholesky decomposition (MCD) and linear covariance models. However, modeling the correlation between responses at each time point is not easy because there is no natural ordering of the responses. In this paper, we use MCD and hypersphere decomposition to model the complex correlation structures for multivariate longitudinal data. We observe that the estimated covariance matrix using the decompositions is positive-definite and can be heteroscedastic and that it is also interpretable. The proposed methods are illustrated using data from a nonalcoholic fatty liver disease study.     

健康早产儿睡眠时闪光视觉诱发电位的自回归分析

本研究旨在探讨闪光视觉诱发电位 (flashvisualevokedpotential,FVEP)与健康早产儿视神经及脑发育间的关系。应用自回归分析法对 36名健康早产儿 (胎龄 2 8周 2天～ 4 2周 )FVEP记录进行了分析 ,平均 8 5个(7～ 11个 )FVEP成分波被检出。依衰减频率分布的直方图将其分为 4组。总功率 (TP)、Ⅰ～Ⅳ组成分波功率(P)、Ⅱ～Ⅳ组衰减时间均随胎龄增加 ,有显著变化 (P <0 0 1orP <0 0 5 )。提示神经系统对闪光刺激的生物电反应在早产儿脑及视神经发育评价上具有重要的临床意义     

A vector autoregressive moving average time series approach for describing asymmetries of antennal control of two millipede species

This article presents a statistical method, vector autoregressive moving average time series analysis, which makes no initial assumptions about the controlling interactions between variables in the data beyond those of linear systems, and has been designed to be statistically valid without requiring several repetitions of data sets. It is therefore very useful for studying physiological and behavioral phenomena.This new methodology is applied to a kinematic analysis of antennal scanning movements in two species of millipede. The analysis demonstrates features of the generation of the antennal and head movements, the direction of information flow within the central nervous system and consequent asymmetric control relationships between bilaterally homologous body parts.     

Perceived morningness–eveningness predicts academic adjustment and substance use across university,but social jetlag is not to blame

Past research has consistently found that evening-types typically report poorer academic adjustment and higher levels of substance use compared to morning-types. An important development within the morningness–eveningness and psychosocial adjustment literature has been the hypothesis that social jetlag (i.e. the asynchrony between an individual’s “biological” and “social” clocks) is one factor that may explain why evening-types are at a greater risk for negative psychosocial adjustment. Yet, only a handful of studies have assessed social jetlag. Furthermore, the few studies that have assessed social jetlag have done so only with concurrent data, and thus have not been able to determine the direction of effects among morningness–eveningness, social jetlag and psychosocial adjustment. To address this important gap in the literature, the present 3-year longitudinal study employed the use of a cross-lagged auto-regressive model to specifically examine the predictive role of perceived morningness–eveningness and social jetlag on two important indices of psychosocial adjustment among university students: academic adjustment and substance use. We also assessed whether there would be an indirect effect between perceived morningness–eveningness and psychosocial adjustment through social jetlag. Participants were 942 (71.5% female; M?=?19 years, SD?=?0.90) undergraduates at a mid-sized university in Southern Ontario, Canada, who completed a survey at three assessments, each one year apart, beginning in first-year university. Measures were demographics (age, gender and parental education), sleep problems, perceived morningness–eveningness, social jetlag, academic adjustment and substance use. As hypothesized, results of path analyses indicated that a greater perceived eveningness preference significantly predicted higher social jetlag, poorer academic adjustment and higher substance use over time. In contrast, we found no support for social jetlag as a predictor of academic adjustment and substance use, indicating that social jetlag did not explain the link between perceived morningness–eveningness and negative psychosocial adjustment. An important finding was the significant predictive effect of higher substance use on social jetlag over time. Results of the present study highlight the importance of employing a longitudinal framework within which to specifically determine the direction of effects among the study variables in order to validate proposed theoretical models that aim to guide our understanding of how perceived morningness–eveningness, social jetlag, academic adjustment and substance use relate to each other.     

Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models

Disease-mapping models for areal data often have fixed effects to measure the effect of spatially varying covariates and random effects with a conditionally autoregressive (CAR) prior to account for spatial clustering. In such spatial regressions, the objective may be to estimate the fixed effects while accounting for the spatial correlation. But adding the CAR random effects can cause large changes in the posterior mean and variance of fixed effects compared to the nonspatial regression model. This article explores the impact of adding spatial random effects on fixed effect estimates and posterior variance. Diagnostics are proposed to measure posterior variance inflation from collinearity between the fixed effect covariates and the CAR random effects and to measure each region's influence on the change in the fixed effect's estimates by adding the CAR random effects. A new model that alleviates the collinearity between the fixed effect covariates and the CAR random effects is developed and extensions of these methods to point-referenced data models are discussed.     

应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的探讨

目的:探讨应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的可行性。方法:应用SPSSl3.0软件对2005年～2009年宝安区某街道其它感染性腹泻逐月发病率进行ARIMA模型建模拟合,用所得到的模型对2010年各月发病率进行预测,并评价其预测效果。结果:宝安区桌街道其它感染性腹泻发病率每年11月为发病高峰,ARIMA(0,1,1)(0,1,0)12模型是其拟合的最佳模型,其预测结果和实际值绝对误差的绝对值最大为930.47,最小为1.96,平均值214.83,平均相对误差百分比39.04%。结论:模型虽然起到一定的预测效果,但预测精度仍存在误差,可通过积累新的周期数据对ARIMA模型进行修正和重新拟合,也可尝试新的预测方法或其他模型,才能加强和保证预测的精度。     

A Parameter-Elimination Method for Nonlinear Regression with Linear Parameters and Autocorrelated Errors

In this study, we are interested in the problem of estimating the parameters in a nonlinear regression model when the error terms are correlated. Throughout this work, we restrict ourselves to the special case when the error terms follow a pth order stationary autoregressive model (AR(p)). Following the idea of LAWTON and SYLVESTRE (1971) and GALLANT and GOEBEL (1976), a parameter-elimination method is proposed, which has the advantages that it is not sensitive to the initial values and convergence of the procedure may be more stable because of the reduced dimension of the problem. The parameter-elimination method is compared with the methods by GALLANT and GOEBEL (1976) and GLASBEY (1980) by Monte Carlo Simulation, and the results of applying the first two methods to the real data obtained from the Environmental Protection Administration of the Executive Yuan of the Republic of China are presented.